Fixed Pattern Noise Removal Method

ABSTRACT

A fixed pattern noise removal method for an image sensor includes steps of calculating each compensation value corresponding to each pixel column according to a plurality of compensation pixel values of the each pixel column; and sampling a plurality of active pixel values in an active pixel area and compensating the plurality of active pixel values according to the each compensation value of the each pixel column, to generate a plurality of compensated active pixel values. A plurality of active pixels sense light to generate the plurality of active pixel values.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a fixed pattern noise removal method,and more particularly, to a fixed pattern noise removal method in animage sensor which only needs to calculate each compensation valuecorresponding to each pixel column for compensation, so as to savememory usage and reduce computation complexity.

2. Description of the Prior Art

Generally for an image sensor, the pixels used for image sensing are notuniform in dimension, spacing, and efficiency. Also, a row sampler suchas the correlation double sampling (CDS) sampling active pixel valuesrow by row may result in deviations. That is, a specific sampler is usedfor a specific column pixel, and thus if samplers in a row are notuniformly designed or fabricated, sampled pixel values of pixel columnsmay have deviation, which causes noise in vertical lines of the imagedue to the sampling non-uniformity. Thus, the image captured by theimage sensor may have a non-random fixed pattern noise (FPN) induced bythe drawback of the hardware architecture.

For example, please refer to FIG. 1A and FIG. 1B, which illustrateschematic diagrams of a real image IMG and a noise image NIMG with acolumn fixed pattern noise, respectively. As shown in FIG. 1A and FIG.1B, since the specific sampler is used for a specific column pixel, andsamplers in a row are not uniformly designed and fabricated, the sampledpixel values of the pixel columns have fixed pattern noises in verticallines of the image (the vertical stripes as shown in FIG. 1B) due to thesampler non-uniformity.

In such a situation, a conventional technology takes a real picture andan optical black picture separately under the same condition, and thensubtracts the optical black picture from the real picture to remove thefixed pattern noise. However, this method requires the image sensor torecord and store the whole optical black picture for different parametercontrol (e.g., temperature, gain value, exposure time), which consumes ahuge memory. Thus, there is a need for improvement of the prior art.

SUMMARY OF THE INVENTION

It is therefore an objective of the present invention to provide a fixedpattern noise (FPN) removal method for an image sensor to compensate thepixel values by only calculating a corresponding compensation value ofeach column pixel, so as to save the memory space and reduce thecomputation complexity.

The present invention discloses a fixed pattern noise (FPN) removalmethod for an image sensor. The FPN removal method includes calculatingeach compensation value corresponding to each pixel column according toa plurality of compensation pixel values of each pixel column; andsampling a plurality of active pixel values in an active pixel area andcompensating the plurality of active pixel values according to the eachcompensation value of each pixel column, to generate a plurality ofcompensated active pixel values; wherein a plurality of active pixelssense light to generate the plurality of active pixel values.

These and other objectives of the present invention will no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiment that isillustrated in the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A and FIG. 1B are schematic diagrams of a real image and a noiseimage with a column fixed pattern noise, respectively.

FIG. 2 is a flowchart of a fixed pattern noise removal process accordingto an embodiment of the present invention.

FIG. 3 is a schematic diagram of an image sensor according to anembodiment of the present invention.

FIG. 4 is a schematic diagram of another image sensor according to anembodiment of the present invention.

FIG. 5 is a schematic diagram of another image sensor according to anembodiment of the present invention.

FIGS. 6 to 8 are schematic diagrams of other three image sensorsaccording to embodiments of the present invention.

DETAILED DESCRIPTION

Please refer to FIG. 2, which is a flowchart of a fixed pattern noise(FPN) removal process 20 according to an embodiment of the presentinvention. As shown in FIG. 2, the fixed pattern removal process 20 isused in an image sensor, and includes the following steps:

Step 200: Start.

Step 202: Calculates each compensation value corresponding to each pixelcolumn according to a plurality of compensation pixel values of eachpixel column.

Step 204: Samples a plurality of active pixel values in an active pixelarea and compensating the plurality of active pixel values according tothe each compensation value of the each pixel column, to generate aplurality of compensated active pixel values; wherein the plurality ofactive pixels sense light to generate the plurality of active pixelvalues.

Step 206: End.

According to the fixed pattern removal process 20, the present inventioncalculates each compensation value corresponding to each pixel columnaccording to a plurality of compensation pixel values of each pixelcolumn. Then, the present invention samples a plurality of active pixelvalues in an active pixel area and compensates the plurality of activepixel values according to the each compensation value of the each pixelcolumn, to generate a plurality of compensated active pixel values. Insuch a condition, the present invention only requires storing thecorresponding compensation values of each column pixel of all pixelcolumns in the memory or even does not need to store the correspondingcompensation values. Therefore, compared with the conventional techniquethat requires storing the entire optical black picture, the presentinvention may significantly save the memory space. As a result, thepresent invention only needs to calculate the corresponding compensationvalues of the pixel columns to compensate, and thereby saves the memoryspace and reduces the computation complexity of the fixed pattern noiseremoval.

For example, please refer to FIG. 3, which illustrates a schematicdiagram of an image sensor 30 according to an embodiment of the presentinvention. The image sensor 30 generates an active pixel value CAPV_(i)for an image processing unit 32 to generate an image data ID. The imagesensor 30 includes a pixel matrix 300, a sampler 302, a memorycontroller 304, line memories 306, 308, multipliers 310, 312, an adder314, a subtractor 316, and an analog to digital converter (ADC) 318. Thepixel matrix 300 includes an active pixel area 320 and an optical blackarea 322, and the pixel matrix 300 is with a Bayer pattern structure sothat each pixel is one of three primary colors, such as a red pixel R,green pixels Gr, Gb, and a blue pixel B, to sense a specific color.

In short, under an initial state, the image sensor 30 may set the ISO toa specific operational gain such that the sampler 302 may sample underthe specific operational gain and then calculate the differences,LM_(306(c)j), LM_(308(c)k), between each primary color pixel columnaverage sum (CP_((c)))/CPN_((c)j) sum (CP_((c)k))/CPN_((c)k) of eachprimary color in each pixel column in the optical black area 322 andeach primary color pixel average AVE_(j), AVE_(k) of each primary colorin the optical black area 322. The difference between the average pixelvalue of specific primary color pixels in a specific pixel column andthe average pixel value of all specific primary color pixels is thefixed pattern noise. Then, the memory controller 304 stores the valuesinto the line memories 306, 308. (The pixels in the optical black area322 do not sense light. In this embodiment, 16 lines are used foraveraging the values, and the pixel values of each primary color pixelin each pixel column of the optical black area 322 correspond to theplurality of compensation pixel values mentioned in the step 202.) Theabove operations can be expressed as follows:

LM_(306(c)j)=AVE_(j)−sum(CP_((c)j))/CPN_((c)j),AVE_(j)=sum(OBP_(j))/OBPN_(j) , j=R,Gr

LM_(308(c)k)=AVE_(k)−sum(CP_((c)k))/CPN_((c)k),AVE_(k)=sum(OBP_(k))/OBPN_(k), k=Gb,B

where sum (CP_((c)j))/CPN_((c)j) and sum (CP_((c)k))/CPN_((c)k)represent the average values derived from dividing the sum of pixelvalues for primary color pixels j, k in a pixel column by the amount ofthe primary color pixels k, and sum (OBP_(j))/OBPN_(j) and sum(OBP_(k))/OBPN_(k) represent the average value derived from dividing thesum of pixel values for the primary color pixels j, k in the opticalblack area 322 by the amount of the primary color pixels j, k.

For example, in the first column of the left-hand side, the Bayerpattern only has the red pixels R and the green pixels Gb. Therefore,when calculating the average value (sum (CP_((1)R))/CPN_((1)R)) of thered pixels R in the first column, the present invention sums up all thepixel values of the red pixels R in the first column and divides the sumby the amount of the red pixel R in the optical black area 322. Next,the present invention calculates the red pixel average value AVE_(R) inthe optical black area 322 by summing up all the pixel values of the redpixels R in the optical black area 322 and dividing the sum by theamount of the red pixels R. Afterward, the difference value LM_(306(1)R)is derived by subtracting sum (CP_((1)R))/CPN_((1)R) from AVE_(R) and issaved in a position corresponding to the red pixel R in the firstcolumn. By the same token, difference values LM_(306 (c)j) LM_(308 (c)k)corresponding to the red pixel R, the green pixels Gr, Gb, and the bluepixel B in the other columns are calculated and stored in the linememories 306, 308, respectively. Since the pixel arrangement repeats inevery other row in the Bayer pattern, only two line memories 306, 308are required to store all the differences of the average values for theprimary color pixels in all the columns. Besides, since the FPN is afixed noise, the specific operational gain may be set to a maximumoperational gain for a definite record.

Next, after the image sensor 30 sets the ISO to the specific operationalgain in the initial state, and the memory controller 304 stores thedifference values LM_(306 (c)j) LM_(308 (c)k) in the line memories 306,308, in an active state, the sampler 302 samples the active area 320 togenerate the active pixel value APV_(i), and the image sensor 30 setsthe ISO to an analog gain AG for the multiplier 310 to amplify theactive pixel value APV_(i) accordingly. Then, the analog-to-digitalconverter 318 converts the amplified active pixel value APV_(i) to adigital format. In the mean time, the memory controller 304 reads thecorresponding difference values LM_(306 (c)j) LM_(308 (c)k) from theline memories 306, 308 to multiply the difference values by a ratio ofan analog gain AG over the specific operational gain in the initialstate via the multiplier 312, so as to obtain the fixed pattern noise(FPN) under the analog gain AG. (Because the FPN is a fixed noise, it isassumed to be proportionally enlarged or reduced according to the gainof ISO). Then, the adder 314 sums up the primary color pixel valueaverage OBA_(i) (the optical black area 322 is not lighted, and thus thespecific primary color pixel value average OBA_(i) in the optical blackarea 322 represents the dark current level of the specific primary colorpixel) in the optical black area 322 under the analog gain AG to obtaineach compensation value for each pixel column. Finally, the subtractor316 subtracts each corresponding compensation value of each pixel columnfrom the active pixel value APV_(i) to obtain the compensated activepixel value CAPV_(i). The above operations can be expressed as follows:

OBA_(i)=sum(AGOBP_(i))/AGOBPN_(i)

CAPV_(i)=APV_(i)−OBA_(i)+LM_((c)i)*AG

i=R,Gr,R,Gr; LM_((c)i)=LM_(306(c)j),LM_(308(c)k)

where sum (AGOBP_(j))/AGOBPN_(j) represents dividing the sum of pixelvalues of primary color pixel i in the optical black area 322 under theanalog gain AG by the amount of primary color pixel i.

As a result, the image sensor 30 only needs to store the differencevalues LM_(306 (c)j) LM_(308 (c)k) in two line memories 306, 308 duringthe initial state, and then compensates and removes the fixed patternnoise via the simple combinations of the multiplier 312, the adder 314,and the subtractor 316 during the active state. Therefore, the computingcomplexity is reduced and the memory space is saved.

Noticeably, the above embodiment calculates each compensation value foreach pixel column for saving memory space and reducing the computingcomplexity. Those skilled in the art can make modifications oralterations accordingly. For example, please refer to FIG. 4, whichillustrates a schematic diagram of another image sensor 40 according toan embodiment of the present invention. The image sensor 40 is similarwith the image sensor 30, so components and signals with the samefunction are represented by the same symbols. The major differencesbetween the image sensor 40 and the image sensor 30 are that the imagesensor 40 does not store the difference values via the memories duringthe initial state. Instead, while the sampler 302 samples and generatesthe active pixel value APV_(i) by sampling the active area during theactive state that followed by an operation of the multiplier 310 and theanalog-to-digital converter 318, the computing unit 400 samples andcalculates each optical black column pixel average value OBCA_(i) in theoptical black area 322 for each pixel column as the compensation valueof each pixel column (the column pixel average includes the dark currentcomponent and the fixed pattern noise component; each column pixelvalues of the each pixel column in the optical black area 322 correspondto the plurality of compensation values of each pixel column in the step202). Then, a subtractor 402 subtracts each corresponding compensationvalue (i.e., the optical black column pixel average value OBCA_(i)) fromthe active pixel value APV_(i) to obtain the compensated active pixelvalue CAPV_(i)′. The above operations can be expressed as follows:

OBCA_(i)=sum(OBCP_(i))/OBCPN_(i)

CAPV_(i)′=APV_(i)−OBCA_(i)

i=R,Gr,R,Gr

where (OBCP_(j))/OBCPN_(j) represents dividing the sum of pixel valuesof the primary color pixel i in a specific column in the optical blackarea 322 under the analog gain AG by the amount of the primary colorpixel i.

As a result, because the image sensor 40 directly samples and calculateseach optical black column pixel average value OBCA_(i) of each pixelcolumn in the optical black area 322 as the compensation value, and thenuses the subtractor 402 to compensate and remove the fixed patternnoise, the image sensor 40 does not require the memories but can stillreduce the computation complexity.

Moreover, please refer to FIG. 5, which shows a schematic diagram ofanother image sensor 50 according to an embodiment of the presentinvention. The image sensor 50 is similar with the image sensor 30, socomponents and signals with the same function are represented by thesame symbols. The major differences between the image sensor 50 and theimage sensor 30 are that, after ISO is set to a specific operationalgain in the initial state, the image sensor 50 cuts the connectionbetween the sampler 502 and the pixels in the active area 520 of thepixel matrix 500 (since the connection is cut between the sampler 502and the pixel matrix 500, the data sampled by the sampler is circuitvalues corresponding to the pixel matrix 500). Then, the image sensor 50calculates the difference values LM_(506 (c)j), LM_(508 (c)k) betweenthe column primary color circuit average sum (CC_((c)j))/CCN_((c)j), sum(CC_((c)k))/CCN_((c)k) of each primary color pixel in each pixel columnof the sampler 502 corresponding to the active area 520 and the primarycircuit value average CAVE_(j), CAVE_(k) of each primary color pixel ofthe sampler 502 that corresponds to the active area 520 (i.e., thedifference value between the specific primary pixel circuit valueaverage of a specific column and the primary color pixel circuit valueaverage for all circuit values of a specific primary color is the fixedpattern noise caused by the sampler 502 that corresponds to a specificprimary color pixel in a specific column). Then, a memory controller 504stores the difference in the line memories 506, 508 (each primary colorcircuit value average of each pixel column in the active area 520 is theplurality of compensation value of each pixel column in the step 202).The above operations can be expressed as follows:

LM_(506(c)j)=CAVE_(j)−sum(CC_((c)j))/CCN_((c)j)

CAVE_(j)=sum(APC_(j))/APCN_(j) , j=R,Gr

LM_(508(c)k)=CAVE_(k)−sum CC_((c)k))/CCN_((c)k)

CAVE_(k)=sum(APC_(k))/APCN_(k) , k=R,Gr

where sum (APC_(j))/APCN_(j), sum (APC_(k))/APCN_(k) represent dividingthe sum of the circuit values of primary color pixel j, k for thesampler 502 that corresponds to the active area 502 by the amount of theprimary pixels j, k, respectively.

For example, in the first column of the left-hand side, the Bayerpattern only has the red pixel R and the green pixel Gb. Therefore, whenthe column red circuit average sum (CC_((1)R)) CCN_((1)R) of the firstcolumn is calculated, the connection between the sampler 502 and thepixel column 500 may be cut first. Next, the sampler simulates thenormal operation of the connection with the red pixel R in the firstcolumn of the active area 520 to obtain the corresponding circuit value,sums the values up, and divides the sum by the amount of the red pixel Rin the first column. Then, the red circuit value average CAVE_(R) of thered pixel R corresponding to the active area 520 is calculated, i.e.,the sampler 502 simulates the normal operation of that connection withall the red pixels R in the active area 520 to obtain the correspondingcircuit value to sum them up and divide the sum by the number of the redpixels R. After that, the sampler 502 subtracts the obtained column redcircuit average sum (CC_((1)R))/CCN_((1)R) from the red circuit valueaverage CAVE_(R) to derive the difference value LM_(506(1)R) and storethe value into the place that corresponds with the first column of thered pixel R. By the same token, difference values LM_(506(c)j),LM_(508(c)k) corresponding to the red pixel R, the green pixels Gr, Bg,and the blue pixel B in the other columns may be stored into thememories 506, 508, respectively. Since the pixel arrangement repeats inevery other row in the Bayer pattern, only two line memories 506, 508are required to store all the difference average for all columns'primary color pixels. Besides, because the FPN is a fixed noise, thespecific operational gain may be set to a maximum operational gain for adefinite record.

Next, after the image sensor 50 sets the ISO to the specific operationalgain in the initial state, cuts the connection between the sampler 502and the pixel matrix 500, and the memory controller 504 stores thedifference values LM_(506(c)j), LM_(508(c)k) in the line memories 506,508, the sampler 502 reconnects the pixel matrix 500 and samples theactive area 520 to generate the active pixel value APV_(i) in an activestate of the image sensor 50. In addition, the image sensor 50 sets theISO to an analog gain AG for the multiplier 310 to amplify the activepixel value APV_(i). Then, the analog-to-digital converter 318 convertsthe amplified APV_(i) to a digital format. In the mean time, the memorycontroller 504 reads the corresponding difference values LM_(506(c)j),LM_(508(c)k) from the line memories 506, 508, and difference valuesLM_(506(c)j), LM_(508(c)k) are multiplied via the multiplier 512 by aratio of an analog gain AG over a specific operational gain in theinitial state to obtain the fixed pattern noise (FPN) under the analoggain AG (i.e. Since the FPN is the fixed noise, the FPN isproportionally enlarged or reduced according to the gain). Then, anadder 314 is used to sum up the primary color pixel value averageOBA_(i) (the optical black area 322 is not lighted, so the specificprimary color pixel value average OBA_(i) represents the dark currentlevel of the specific primary color pixel) in the optical black area 322under the analog gain AG to obtain each compensation value for eachpixel column. Finally, the subtractor 316 subtracts each correspondingcompensation value of each pixel column from the active pixel valueAPV_(i) to obtain the compensated active pixel value CAPV_(i) for theactive area. The above operations can be expressed as follows:

OBA_(i)=sum(AGOBP_(i))/AGOBPN_(i)

CAPV_(i)″=APV_(i)−OBA_(i)+LM_((c)j)*AG

i=R,Gr,R,Gr; LM_((c)j)=LM_(506(c)j),LM_(508(c)k)

where sum (AGOBP_(j))/AGOBPN_(j) represents dividing the sum of pixelvalues of the primary color pixel i in the optical black area 322 underthe analog gain AG by the amount of the primary color pixel i.

As a result, the image sensor 50 only needs to store the differencevalues LM_(506 (c)j), LM_(508 (c)k) in two line memories 506, 508 duringthe initial state, and then compensates and removes the fixed patternnoise via the simple combinations of the multiplier 312, the adder 314,and the subtractor 316 during the active state. Therefore, the computingcomplexity is reduced and the memory space is saved. Moreover, in theimage sensor 50, the connection between the sampler 502 and the pixelmatrix 500 is cut, and each pixel column compensation value iscalculated by each primary color circuit average of each column pixel.Therefore, compared with the image sensor 30, the image sensor 50 mayuse smaller optical black area (i.e. the image sensor 50 does notrequire the optical black area 522 for averaging and calculating thecompensation value, and thus only four columns are needed).

Besides, in the above embodiments, the image sensors 30, 40, and 50compensate the active pixel value APV_(i) with the compensation value ina digital manner (i.e., the compensation value is in a digital format,and the active pixel value APV_(i) is converted to the digital format byan analog-to-digital converter 318 before processing the compensation).However, in other embodiments, the compensation may be performed in ananalog manner to the compensation value and the active pixel valueAPV_(i). Please refer to FIG. 6 to FIG. 8, which are schematic diagramsof image sensors 60, 70, and 80 according to other three embodiments ofthe present invention. The image sensors 60, 70, and 80 are similar withthe image sensors 30, 40, and 50, so components and signals with thesame function are represented by the same symbols. The major differencesbetween the image sensors 60, 70, and 80 and the image sensors 30, 40,and 50 are that the image sensors 60, 70, and 80 convert the digitalcompensation value to the analog compensation value via digital toanalog converters 600, 700, and 800, respectively, and then compensatethe analog amplified active pixel value APV_(i) through the subtractors316′, 402′. Afterward, the compensated active pixel value is convertedto the digital format by an analog-to-digital converter 318′.Consequently, the image sensors 60, 70, and 80 may compensate thecompensation value and the active pixel value APV_(i) in the analogmanner.

In the prior art, the image sensor needs to separately take a realpicture and an optical black picture, and then subtracts the opticalblack picture form the real picture to remove the fixed pattern noise.However, using the conventional method requires to record and store thewhole optical black for different parameter control (temperature, gainvalue, exposure time), which requires a huge memory.

In comparison, the present invention calculates the compensation valuecorresponding with each pixel column for compensation. Therefore, thepresent invention only needs to store each compensation valuecorresponding to each pixel column in the memory, or does not need tostore the compensation value in the memory, which reduces the memoryspace as well as the computing complexity.

Those skilled in the art will readily observe that numerousmodifications and alterations of the device and method may be made whileretaining the teachings of the invention. Accordingly, the abovedisclosure should be construed as limited only by the metes and boundsof the appended claims.

What is claimed is:
 1. A fixed pattern noise (FPN) removal method for animage sensor, comprising: calculating each compensation valuecorresponding to each pixel column according to a plurality ofcompensation pixel values of the each pixel column; and sampling aplurality of active pixel values in an active pixel area andcompensating the plurality of active pixel values according to the eachcompensation value of the each pixel column, to generate a plurality ofcompensated active pixel values; wherein a plurality of active pixelssense light to generate the plurality of active pixel values.
 2. The FPNremoval method of claim 1, wherein the step of calculating the eachcompensation value corresponding to the each pixel column according tothe plurality of compensation pixel values of the each pixel columncomprises: setting a specific operational gain; and sampling andcalculating a difference between each primary color pixel column averageof pixels of each primary color in the each pixel column in an opticalblack area and each primary color pixel average of pixels of the eachprimary color in the optical black area under the specific operationalgain and an initial state, and saving the difference to a memory;wherein all pixels in the optical black area do not sense light.
 3. TheFPN removal method of claim 2, wherein the step of calculating the eachcompensation value corresponding to the each pixel column according tothe plurality of compensation pixel values of the each pixel columncomprises: calculating each optical black area specific primary colorpixel average of pixels of all specific primary colors in the opticalblack area under an analog gain; and multiplying the difference betweenthe each primary color pixel column average of the pixels of the eachprimary color in the each pixel column in an optical black area and theeach primary color pixel average of the pixels of the each primary colorin the optical black area by a ratio of the analog gain to the specificoperational gain, and then summing up the each optical black areaspecific primary color pixel average as the each compensation value ofthe each pixel column.
 4. The FPN removal method of claim 2, wherein thespecific operational gain is a maximum operational gain.
 5. The FPNremoval method of claim 1, wherein the step of calculating the eachcompensation value corresponding to the each pixel column according tothe plurality of compensation pixel values of the each pixel columncomprises: sampling and calculating each optical black area pixel columnaverage of the each pixel column in an optical black area as the eachcompensation value of the each pixel column when the plurality of activepixels in the active pixel area sense light.
 6. The FPN removal methodof claim 1, wherein the step of calculating the each compensation valuecorresponding to the each pixel column according to the plurality ofcompensation pixel values of the each pixel column comprises: setting aspecific operational gain; cutting off connections between a sampler andthe plurality of active pixels in the active pixel area; and calculatinga difference between each primary color circuit column average of pixelsof each primary color in the each pixel column in the active pixel areacorresponding to the sampler and each primary color circuit average ofpixels of the each primary color in the active pixel area correspondingto the sampler under the specific operational gain and an initial state,and saving the difference to a memory.
 7. The FPN removal method ofclaim 1, wherein the step of calculating the each compensation valuecorresponding to the each pixel column according to the plurality ofcompensation pixel values of the each pixel column comprises:calculating an optical black area pixel average of all pixels in anoptical area; and multiplying the difference between the each primarycolor circuit average of the pixels of the each primary color in theactive pixel area corresponding to the sampler and the each primarycolor circuit column average of the pixels of the each primary color inthe each pixel column in the active pixel area corresponding to thesampler by a ratio of an analog gain to the specific operational gain,and then summing up the optical black area pixel average as the eachcompensation value of the each pixel column; wherein all pixels in theoptical black area do not sense light.
 8. The FPN removal method ofclaim 6, wherein the specific operational gain is a maximum operationalgain.
 9. The FPN removal method of claim 1, wherein the eachcompensation value and the plurality of active pixel values arecompensated in a digital form.
 10. The FPN removal method of claim 1,wherein the each compensation value and the plurality of active pixelvalues are compensated in an analog form.